基于小波变换的ARMA-LSSVM短期风速预测

对风电场风速的准确预测,可以有效减轻并网后风电对电网的影响,提高风电市场竞争力。提出将时间序列自回归滑动平均模型（Auto Regressive Moving Average, ARMA） 与最小二乘支持向量机模型（Least Square Support Vector Machine,LS-SVM）相结合的混合模型短期风速预测方法。采用小波变换（Wavelet Transform,WT）方法将历史风速序列分解成具有不同频率特征的序列。根据分解后各分量的特点,对于低频趋势分量选取LS-SVM方法进行预测,而高频波动分量则选取ARMA模型进行预测,采用小波重构得到最终预测结果。仿真实例表明,不同的预测方法整体的预测精度不同,而混合模型预测的均方根误差最低为11.5%,与单一预测方法相比,混合模型提高了预测精度。

Short-term wind speed forecasting model based on ARMA-LSSVM and wavelet transform

A wind speed forecasting with high accuracy can effectively reduce or avoid the adverse effect of wind farm on power grids,meanwhile it can enhance the competitive ability of wind power in electricity market.A short-term wind speed forecasting method based on auto-regressive moving average(ARMA) model and least square support vector machine(LS-SWM) model was proposed.By using wavelet transform method,the historical load data was decomposed into series with different frequency characteristics.The low frequency components were predicted by LS-SVM model,while the high frequency components were predicted by ARMA model.The final forecasting results were obtained with wavelet reconstruction.Research results show that the prediction accuracy is different from each method.The mean square error of the proposed hybrid forecast model is 11.5%,better than the results by single forecasting methods.